Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, St Joseph's Healthcare Hamilton, McMaster University, 100 West 5th Street, Hamilton, Ontario, Canada, L8N 3K7.
Curr Psychiatry Rep. 2013 Sep;15(9):388. doi: 10.1007/s11920-013-0388-x.
Clinical experience and research findings suggest that schizophrenia is a disorder comprised of multiple genetic and neurophysiological subtypes with differential response to treatment. Electroencephalography (EEG) is a non-invasive, inexpensive and useful tool for investigating the neurobiology of schizophrenia and its subtypes. EEG studies elucidate the neurophysiological mechanisms potentially underlying clinical symptomatology. In this review article recent advances in applying EEG to study pathophysiology, phenomenology, and treatment response in schizophrenia are discussed. Investigative strategies employed include: analyzing quantitative EEG (QEEG) spectral power during the resting state and cognitive tasks; applying machine learning methods to identify QEEG indicators of diagnosis and treatment response; and using the event-related brain potential (ERP) technique to characterize the neurocognitive processes underlying clinical symptoms. Studies attempting to validate potential EEG biomarkers of schizophrenia and its symptoms, which could be useful in assessing familial risk and treatment response, are also reviewed.
临床经验和研究结果表明，精神分裂症是一种由多种遗传和神经生理亚型组成的疾病，对治疗的反应不同。脑电图 (EEG) 是一种非侵入性、成本低廉且有用的工具，可用于研究精神分裂症及其亚型的神经生物学。EEG 研究阐明了潜在的临床症状的神经生理机制。在这篇综述文章中，讨论了将 EEG 应用于研究精神分裂症的病理生理学、现象学和治疗反应的最新进展。所采用的研究策略包括：分析静息状态和认知任务期间的定量脑电图 (QEEG) 频谱功率；应用机器学习方法识别 QEEG 诊断和治疗反应的指标；以及使用事件相关脑电位 (ERP) 技术来描述临床症状的神经认知过程。还回顾了试图验证精神分裂症及其症状的潜在 EEG 生物标志物的研究，这些标志物可能有助于评估家族风险和治疗反应。
